Gareth Simpson's slides from TechSEO Boost 2019

1. Gareth Simpson | @seekerdigital | #TechSEOBoost
Gareth Simpson
@simpsongareth
NLP Powered
Outreach Link Building

2. Gareth Simpson | @seekerdigital | #TechSEOBoost
Specialist Outreach and Link Agency

3. Gareth Simpson | @seekerdigital | #TechSEOBoost

4. Gareth Simpson | @seekerdigital | #TechSEOBoost
What we’ll be covering
1. Link building process
2. NLP productivity hacks

5. Gareth Simpson | @seekerdigital | #TechSEOBoost
The Four Pillars of SEO
• Content: Relevance & keywords
• User Experience: Engagement & reputation
• Links: Authority & trust
• Technical: Website build quality

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The Importance of Backlinks
https://ahrefs.com/blog/competitive-analysis/
“Because when we analysed over 2 million keywords, we also
found that it was possible to outrank sites with higher DR
scores by building backlinks to individual pages.
So if your site is new and is in a competitive niche with high DR
scores, then your initial plan should be to build links directly to
pages you want to rank.
I should also point out that Google deny overall domain authority
influences rankings, however, all our testing finds a strong
positive correlation between DR and rankings. That being said,
as we also found links to individual pages trump overall DR…”

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Link Building Process

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9. Gareth Simpson | @seekerdigital | #TechSEOBoost

10. Gareth Simpson | @seekerdigital | #TechSEOBoost

11. Gareth Simpson | @seekerdigital | #TechSEOBoost

12. Gareth Simpson | @seekerdigital | #TechSEOBoost

13. Gareth Simpson | @seekerdigital | #TechSEOBoost
The Outreach Problem
• Link building is hard to scale
• Requires effective PM
• Mundane work
• Lots of data to process

14. Gareth Simpson | @seekerdigital | #TechSEOBoost
12.5% of staff time is lost in data
collection. That’s five hours a
week in a 40-hour work week.SOURCE: GARTNER

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Data Piping
• Platforms
• Ahrefs & Majestic
• Pitchbox
• Data studio / Sheets
• Connections
• Zapier
• Piesync
• API

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Decision Bottlenecks
• Human intervention
• Read the situation & act
• Blockers in the workflow

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https://www.slideshare.net/ipullrank/building-your-outreach-machine
Published on June 9th, 2017

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ML Benefits for Outreach
• Automating decisions
• Processing mass amounts of data
• Enables you to work faster, smarter and with more accuracy
• Frees up smart people from doing mundane work
• Higher level applications offering more developed models

19. Gareth Simpson | @seekerdigital | #TechSEOBoost
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Natural Language Processing
• Content classification
• Natural language understanding
• Provides awareness to:
• Automate repetitive tasks
• Emulate human decisions
2
0

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NLP without a PHD

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App stack
2
DATA ML ENGINES

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NLP Powered Outreach
ENABLING BETTER COMMUNICATIONS

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Contact Research & Enrichment
1. Manual contact research
2. Identify the appropriate person
3. Slow & error prone

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27. Gareth Simpson | @seekerdigital | #TechSEOBoost
The Process
1. Pull job title from data source e.g. Pitchbox or Vuelio
2. Run through MonkeyLearn’s job industry classifier
3. Filter table on contacts relevant for pitch
4. Run through MonkeyLearn’s Seniority Classifier to ID
5. Enrich contacts for additional info e.g. social profiles
6. Pipe populated data into Pitchbox for sends.

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Email Filtering
1. Replying to 1000s of emails every month
2. Yes/No/Maybe/Questions/Auto
3. Not best use of our expert negotiators
time

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Cut through the noise
• We’re sitting of 4 years of email decisions
which became our training data
• Summarise email
• Classify for intent, sentiment, urgency etc
• Send to Pitchbox as custom tag for filtering
2
9

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Training data
3
1
2
3
4
Human processed data
Features & tags
Good data in > good data out
Refine over time

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By 2020, 85% of
customer interactions
will be handled
without a human
SOURCE: GARTNER

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Reactive PR
• HARO
• Twitter
• Response Source

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HARO
1. Journalist submits request
2. PRs check email digests
3. Coordinate content and respond
4. Journalist uses quote and credits

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Twitter
• #journorequest
• #prrequest
• #bloggerrequest

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Categories
Categorize your content using a five-level classification hierarchy.
Concepts
Identify high-level concepts that aren't necessarily directly referenced in the
text.
Emotions
Analyze emotion conveyed by specific target phrases or by the document as
a whole.
Entities
Find people, places, events, and other types of entities mentioned in your
content.
Keywords
Search your content for relevant keywords.
Metadata
For HTML and URL input, get the author of the webpage, the page title, and
the publication date.
Relations
Recognize when two entities are related, and identify the type of relation.
Semantic Role
Parse sentences into subject-action-object form, and identify entities and
keywords that are subjects or objects of an action.
Sentiment
Analyze the sentiment toward specific target phrases and the sentiment of
the document as a whole.
Custom Models
Identify custom entities and relations unique to your domain with Watson
Knowledge Studio.

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HARO Email Extraction
4

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#journorequest
4
FILTERCLASSIFY NOTIFICATIONSHASHTAGS

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Reactive PR
1. Digital PRs log in once per day
2. Filter by keyword / category
3. Export contact & info request to Pitchbox.

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Summary
• Map out a process for link building
• Leverage APIs to speed up data transfer
• Use NLP to help with automating decisions
• Start with MonkeyLearn & Zapier, then move to
native API
Get them links!

44. Gareth Simpson | @seekerdigital | #TechSEOBoost

45. Gareth Simpson | @seekerdigital | #TechSEOBoost
Thank You
–
gareth@seeker.digital